Overview

Dataset statistics

Number of variables19
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 KiB
Average record size in memory156.0 B

Variable types

DateTime1
Numeric17
Categorical1

Alerts

civil_conflicts is highly correlated with state_intervention and 15 other fieldsHigh correlation
state_intervention is highly correlated with civil_conflicts and 13 other fieldsHigh correlation
conflict_between_states is highly correlated with totalHigh correlation
total is highly correlated with civil_conflicts and 1 other fieldsHigh correlation
gni_ger is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gni_fra is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gni_ita is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gni_jpn is highly correlated with civil_conflicts and 13 other fieldsHigh correlation
gni_can is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gni_rus is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gni_usa is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gni_gbr is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gni_bra is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gni_ind is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gni_mex is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gni_zaf is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gni_chn is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gni_wld is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
civil_conflicts is highly correlated with gni_ger and 12 other fieldsHigh correlation
state_intervention is highly correlated with total and 12 other fieldsHigh correlation
conflict_between_states is highly correlated with totalHigh correlation
total is highly correlated with state_intervention and 1 other fieldsHigh correlation
gni_ger is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gni_fra is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gni_ita is highly correlated with civil_conflicts and 13 other fieldsHigh correlation
gni_jpn is highly correlated with civil_conflicts and 13 other fieldsHigh correlation
gni_can is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gni_rus is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gni_usa is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gni_gbr is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gni_bra is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gni_ind is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gni_mex is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gni_zaf is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gni_chn is highly correlated with state_intervention and 13 other fieldsHigh correlation
gni_wld is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
civil_conflicts is highly correlated with gni_can and 6 other fieldsHigh correlation
state_intervention is highly correlated with gni_ger and 8 other fieldsHigh correlation
gni_ger is highly correlated with state_intervention and 13 other fieldsHigh correlation
gni_fra is highly correlated with gni_ger and 12 other fieldsHigh correlation
gni_ita is highly correlated with gni_ger and 12 other fieldsHigh correlation
gni_jpn is highly correlated with gni_ger and 9 other fieldsHigh correlation
gni_can is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gni_rus is highly correlated with gni_ger and 10 other fieldsHigh correlation
gni_usa is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gni_gbr is highly correlated with civil_conflicts and 12 other fieldsHigh correlation
gni_bra is highly correlated with state_intervention and 13 other fieldsHigh correlation
gni_ind is highly correlated with civil_conflicts and 13 other fieldsHigh correlation
gni_mex is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gni_zaf is highly correlated with gni_ger and 12 other fieldsHigh correlation
gni_chn is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
gni_wld is highly correlated with civil_conflicts and 14 other fieldsHigh correlation
year is highly correlated with civil_conflicts and 17 other fieldsHigh correlation
civil_conflicts is highly correlated with year and 4 other fieldsHigh correlation
state_intervention is highly correlated with year and 9 other fieldsHigh correlation
conflict_between_states is highly correlated with year and 5 other fieldsHigh correlation
total is highly correlated with year and 7 other fieldsHigh correlation
gni_ger is highly correlated with year and 13 other fieldsHigh correlation
gni_fra is highly correlated with year and 14 other fieldsHigh correlation
gni_ita is highly correlated with year and 9 other fieldsHigh correlation
gni_jpn is highly correlated with year and 7 other fieldsHigh correlation
gni_can is highly correlated with year and 12 other fieldsHigh correlation
gni_rus is highly correlated with year and 12 other fieldsHigh correlation
gni_usa is highly correlated with year and 16 other fieldsHigh correlation
gni_gbr is highly correlated with year and 11 other fieldsHigh correlation
gni_bra is highly correlated with year and 14 other fieldsHigh correlation
gni_ind is highly correlated with year and 15 other fieldsHigh correlation
gni_mex is highly correlated with year and 13 other fieldsHigh correlation
gni_zaf is highly correlated with year and 14 other fieldsHigh correlation
gni_chn is highly correlated with year and 13 other fieldsHigh correlation
gni_wld is highly correlated with year and 14 other fieldsHigh correlation
year has unique values Unique
gni_ger has unique values Unique
gni_fra has unique values Unique
gni_ita has unique values Unique
gni_jpn has unique values Unique
gni_can has unique values Unique
gni_rus has unique values Unique
gni_usa has unique values Unique
gni_gbr has unique values Unique
gni_bra has unique values Unique
gni_ind has unique values Unique
gni_mex has unique values Unique
gni_zaf has unique values Unique
gni_chn has unique values Unique
gni_wld has unique values Unique

Reproduction

Analysis started2022-07-04 11:41:32.511837
Analysis finished2022-07-04 11:42:03.409054
Duration30.9 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

year
Date

HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
Minimum1970-01-01 00:00:00.000001
Maximum1970-01-01 00:00:00.000002
2022-07-04T13:42:03.502075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:42:03.718124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)

civil_conflicts
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)46.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.21875
Minimum23
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:42:03.818147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile25.2
Q128
median31
Q334.25
95-th percentile44.9
Maximum48
Range25
Interquartile range (IQR)6.25

Descriptive statistics

Standard deviation6.121007586
Coefficient of variation (CV)0.1899827767
Kurtosis1.045391895
Mean32.21875
Median Absolute Deviation (MAD)3
Skewness1.153251821
Sum1031
Variance37.46673387
MonotonicityNot monotonic
2022-07-04T13:42:03.917169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
324
12.5%
284
12.5%
293
9.4%
273
9.4%
313
9.4%
303
9.4%
442
 
6.2%
372
 
6.2%
232
 
6.2%
331
 
3.1%
Other values (5)5
15.6%
ValueCountFrequency (%)
232
6.2%
273
9.4%
284
12.5%
293
9.4%
303
9.4%
313
9.4%
324
12.5%
331
 
3.1%
341
 
3.1%
351
 
3.1%
ValueCountFrequency (%)
481
 
3.1%
461
 
3.1%
442
6.2%
372
6.2%
361
 
3.1%
351
 
3.1%
341
 
3.1%
331
 
3.1%
324
12.5%
313
9.4%

state_intervention
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct14
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.25
Minimum2
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:42:04.009190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q14
median5.5
Q39
95-th percentile22.8
Maximum25
Range23
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.900210375
Coefficient of variation (CV)0.8363891364
Kurtosis0.755369208
Mean8.25
Median Absolute Deviation (MAD)2.5
Skewness1.413726976
Sum264
Variance47.61290323
MonotonicityNot monotonic
2022-07-04T13:42:04.105212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
45
15.6%
54
12.5%
34
12.5%
23
9.4%
63
9.4%
73
9.4%
92
 
6.2%
252
 
6.2%
81
 
3.1%
131
 
3.1%
Other values (4)4
12.5%
ValueCountFrequency (%)
23
9.4%
34
12.5%
45
15.6%
54
12.5%
63
9.4%
73
9.4%
81
 
3.1%
92
 
6.2%
131
 
3.1%
181
 
3.1%
ValueCountFrequency (%)
252
6.2%
211
 
3.1%
201
 
3.1%
191
 
3.1%
181
 
3.1%
131
 
3.1%
92
6.2%
81
 
3.1%
73
9.4%
63
9.4%

conflict_between_states
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size384.0 B
2
12 
1
10 
0
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.1%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row0

Common Values

ValueCountFrequency (%)
212
37.5%
110
31.2%
09
28.1%
31
 
3.1%

Length

2022-07-04T13:42:04.203234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T13:42:04.314259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
212
37.5%
110
31.2%
09
28.1%
31
 
3.1%

Most occurring characters

ValueCountFrequency (%)
212
37.5%
110
31.2%
09
28.1%
31
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number32
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
212
37.5%
110
31.2%
09
28.1%
31
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common32
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
212
37.5%
110
31.2%
09
28.1%
31
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII32
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
212
37.5%
110
31.2%
09
28.1%
31
 
3.1%

total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct19
Distinct (%)59.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.625
Minimum31
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:42:04.412281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile32
Q135.75
median40
Q349
95-th percentile54.45
Maximum56
Range25
Interquartile range (IQR)13.25

Descriptive statistics

Standard deviation7.732420228
Coefficient of variation (CV)0.1857638493
Kurtosis-1.076805083
Mean41.625
Median Absolute Deviation (MAD)7
Skewness0.4612043776
Sum1332
Variance59.79032258
MonotonicityNot monotonic
2022-07-04T13:42:04.518305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
405
15.6%
334
12.5%
383
 
9.4%
523
 
9.4%
492
 
6.2%
322
 
6.2%
411
 
3.1%
391
 
3.1%
481
 
3.1%
431
 
3.1%
Other values (9)9
28.1%
ValueCountFrequency (%)
311
 
3.1%
322
 
6.2%
334
12.5%
351
 
3.1%
361
 
3.1%
371
 
3.1%
383
9.4%
391
 
3.1%
405
15.6%
411
 
3.1%
ValueCountFrequency (%)
561
 
3.1%
551
 
3.1%
541
 
3.1%
523
9.4%
511
 
3.1%
492
6.2%
481
 
3.1%
431
 
3.1%
421
 
3.1%
411
 
3.1%

gni_ger
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.894975162 × 1012
Minimum1.414249684 × 1012
Maximum4.105202181 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:42:04.622329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.414249684 × 1012
5-th percentile1.837597572 × 1012
Q12.169307755 × 1012
median2.850861546 × 1012
Q33.65352646 × 1012
95-th percentile3.988334131 × 1012
Maximum4.105202181 × 1012
Range2.690952497 × 1012
Interquartile range (IQR)1.484218705 × 1012

Descriptive statistics

Standard deviation8.265878808 × 1011
Coefficient of variation (CV)0.2855250337
Kurtosis-1.529191
Mean2.894975162 × 1012
Median Absolute Deviation (MAD)7.369139402 × 1011
Skewness-0.02710052998
Sum9.263920519 × 1013
Variance6.832475247 × 1023
MonotonicityNot monotonic
2022-07-04T13:42:04.735354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1.414249684 × 10121
 
3.1%
1.790871643 × 10121
 
3.1%
4.014394172 × 10121
 
3.1%
4.105202181 × 10121
 
3.1%
3.778789184 × 10121
 
3.1%
3.555930678 × 10121
 
3.1%
3.434101057 × 10121
 
3.1%
3.967012279 × 10121
 
3.1%
3.820263806 × 10121
 
3.1%
3.611772219 × 10121
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
1.414249684 × 10121
3.1%
1.790871643 × 10121
3.1%
1.875827877 × 10121
3.1%
1.932123667 × 10121
3.1%
1.937024252 × 10121
3.1%
2.056434684 × 10121
3.1%
2.074905675 × 10121
3.1%
2.137944338 × 10121
3.1%
2.179762228 × 10121
3.1%
2.204518755 × 10121
3.1%
ValueCountFrequency (%)
4.105202181 × 10121
3.1%
4.014394172 × 10121
3.1%
3.967012279 × 10121
3.1%
3.953466259 × 10121
3.1%
3.845325105 × 10121
3.1%
3.820263806 × 10121
3.1%
3.780819607 × 10121
3.1%
3.778789184 × 10121
3.1%
3.611772219 × 10121
3.1%
3.555930678 × 10121
3.1%

gni_fra
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.094296816 × 1012
Minimum1.029131195 × 1012
Maximum2.994136039 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:42:04.845379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.029131195 × 1012
5-th percentile1.273150009 × 1012
Q11.454647662 × 1012
median2.191771058 × 1012
Q32.720668068 × 1012
95-th percentile2.928423213 × 1012
Maximum2.994136039 × 1012
Range1.965004844 × 1012
Interquartile range (IQR)1.266020407 × 1012

Descriptive statistics

Standard deviation6.594464194 × 1011
Coefficient of variation (CV)0.3148772487
Kurtosis-1.74673319
Mean2.094296816 × 1012
Median Absolute Deviation (MAD)6.663648566 × 1011
Skewness-0.05237966676
Sum6.70174981 × 1013
Variance4.3486958 × 1023
MonotonicityNot monotonic
2022-07-04T13:42:04.959405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1.029131195 × 10121
 
3.1%
1.273598362 × 10121
 
3.1%
2.787416525 × 10121
 
3.1%
2.855661376 × 10121
 
3.1%
2.653805222 × 10121
 
3.1%
2.525276586 × 10121
 
3.1%
2.491864987 × 10121
 
3.1%
2.917864654 × 10121
 
3.1%
2.874860245 × 10121
 
3.1%
2.7423493 × 10121
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
1.029131195 × 10121
3.1%
1.272602023 × 10121
3.1%
1.273598362 × 10121
3.1%
1.329198517 × 10121
3.1%
1.387213403 × 10121
3.1%
1.398516068 × 10121
3.1%
1.402495833 × 10121
3.1%
1.406931846 × 10121
3.1%
1.470552933 × 10121
3.1%
1.517047281 × 10121
3.1%
ValueCountFrequency (%)
2.994136039 × 10121
3.1%
2.941328117 × 10121
3.1%
2.917864654 × 10121
3.1%
2.874860245 × 10121
3.1%
2.855661376 × 10121
3.1%
2.787416525 × 10121
3.1%
2.763004002 × 10121
3.1%
2.7423493 × 10121
3.1%
2.713440991 × 10121
3.1%
2.706418758 × 10121
3.1%

gni_ita
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.658543383 × 1012
Minimum9.240833714 × 1011
Maximum2.386641522 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:42:05.070431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum9.240833714 × 1011
5-th percentile1.067642775 × 1012
Q11.234257891 × 1012
median1.8137474 × 1012
Q32.09206409 × 1012
95-th percentile2.248281147 × 1012
Maximum2.386641522 × 1012
Range1.462558151 × 1012
Interquartile range (IQR)8.578061994 × 1011

Descriptive statistics

Standard deviation4.579825512 × 1011
Coefficient of variation (CV)0.2761354065
Kurtosis-1.6464723
Mean1.658543383 × 1012
Median Absolute Deviation (MAD)4.382527063 × 1011
Skewness-0.04130784562
Sum5.307338825 × 1013
Variance2.097480172 × 1023
MonotonicityNot monotonic
2022-07-04T13:42:05.182455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9.240833714 × 10111
 
3.1%
1.166755021 × 10121
 
3.1%
2.02831754 × 10121
 
3.1%
2.11503662 × 10121
 
3.1%
1.9727349 × 10121
 
3.1%
1.882461088 × 10121
 
3.1%
1.823940556 × 10121
 
3.1%
2.162198396 × 10121
 
3.1%
2.138212004 × 10121
 
3.1%
2.08440658 × 10121
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
9.240833714 × 10111
3.1%
1.049906465 × 10121
3.1%
1.082154302 × 10121
3.1%
1.14229934 × 10121
3.1%
1.160406751 × 10121
3.1%
1.164448342 × 10121
3.1%
1.166755021 × 10121
3.1%
1.228446298 × 10121
3.1%
1.236195089 × 10121
3.1%
1.250007311 × 10121
3.1%
ValueCountFrequency (%)
2.386641522 × 10121
3.1%
2.289189704 × 10121
3.1%
2.21481051 × 10121
3.1%
2.198543925 × 10121
3.1%
2.162198396 × 10121
3.1%
2.138212004 × 10121
3.1%
2.131574091 × 10121
3.1%
2.11503662 × 10121
3.1%
2.08440658 × 10121
3.1%
2.02831754 × 10121
3.1%

gni_jpn
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.861832334 × 1012
Minimum3.078857299 × 1012
Maximum6.445536591 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:42:05.293644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3.078857299 × 1012
5-th percentile3.406052584 × 1012
Q14.53700553 × 1012
median4.985164511 × 1012
Q35.233939506 × 1012
95-th percentile6.138341002 × 1012
Maximum6.445536591 × 1012
Range3.366679292 × 1012
Interquartile range (IQR)6.969339759 × 1011

Descriptive statistics

Standard deviation7.637189452 × 1011
Coefficient of variation (CV)0.1570845913
Kurtosis0.8413631088
Mean4.861832334 × 1012
Median Absolute Deviation (MAD)3.789870868 × 1011
Skewness-0.3278592422
Sum1.555786347 × 1014
Variance5.832666273 × 1023
MonotonicityNot monotonic
2022-07-04T13:42:05.407670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
3.078857299 × 10121
 
3.1%
3.156842378 × 10121
 
3.1%
5.323445359 × 10121
 
3.1%
5.230631861 × 10121
 
3.1%
5.113251597 × 10121
 
3.1%
5.177794688 × 10121
 
3.1%
4.619771169 × 10121
 
3.1%
5.079021305 × 10121
 
3.1%
5.391605624 × 10121
 
3.1%
6.445536591 × 10121
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
3.078857299 × 10121
3.1%
3.156842378 × 10121
3.1%
3.609951844 × 10121
3.1%
3.942653569 × 10121
3.1%
4.148975634 × 10121
3.1%
4.243893355 × 10121
3.1%
4.442724323 × 10121
3.1%
4.493789445 × 10121
3.1%
4.551410891 × 10121
3.1%
4.592583679 × 10121
3.1%
ValueCountFrequency (%)
6.445536591 × 10121
3.1%
6.414396892 × 10121
3.1%
5.912477091 × 10121
3.1%
5.592682725 × 10121
3.1%
5.422436227 × 10121
3.1%
5.391605624 × 10121
3.1%
5.323445359 × 10121
3.1%
5.243862441 × 10121
3.1%
5.230631861 × 10121
3.1%
5.22288721 × 10121
3.1%

gni_can
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.118478607 × 1012
Minimum5.477094595 × 1011
Maximum1.818471076 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:42:05.523696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5.477094595 × 1011
5-th percentile5.592823141 × 1011
Q16.119200748 × 1011
median1.076851242 × 1012
Q31.595797135 × 1012
95-th percentile1.78527863 × 1012
Maximum1.818471076 × 1012
Range1.270761616 × 1012
Interquartile range (IQR)9.838770607 × 1011

Descriptive statistics

Standard deviation4.922787727 × 1011
Coefficient of variation (CV)0.4401324885
Kurtosis-1.800302009
Mean1.118478607 × 1012
Median Absolute Deviation (MAD)4.759090595 × 1011
Skewness0.1176319613
Sum3.579131542 × 1013
Variance2.4233839 × 1023
MonotonicityNot monotonic
2022-07-04T13:42:05.743746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
5.477094595 × 10111
 
3.1%
5.746271855 × 10111
 
3.1%
1.719638539 × 10121
 
3.1%
1.695996609 × 10121
 
3.1%
1.628291572 × 10121
 
3.1%
1.509327937 × 10121
 
3.1%
1.532663548 × 10121
 
3.1%
1.776572161 × 10121
 
3.1%
1.818471076 × 10121
 
3.1%
1.79591987 × 10121
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
5.477094595 × 10111
3.1%
5.591725249 × 10111
3.1%
5.593721417 × 10111
3.1%
5.730801688 × 10111
3.1%
5.746271855 × 10111
3.1%
5.850867094 × 10111
3.1%
5.92228332 × 10111
3.1%
6.096560323 × 10111
3.1%
6.126747556 × 10111
3.1%
6.346908855 × 10111
3.1%
ValueCountFrequency (%)
1.818471076 × 10121
3.1%
1.79591987 × 10121
3.1%
1.776572161 × 10121
3.1%
1.759453773 × 10121
3.1%
1.719638539 × 10121
3.1%
1.695996609 × 10121
3.1%
1.628291572 × 10121
3.1%
1.627048915 × 10121
3.1%
1.585379876 × 10121
3.1%
1.532663548 × 10121
3.1%

gni_rus
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.600677326 × 1011
Minimum1.881912284 × 1011
Maximum2.212868847 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:42:05.853771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.881912284 × 1011
5-th percentile2.563793218 × 1011
Q13.95487341 × 1011
median6.618680994 × 1011
Q31.491396098 × 1012
95-th percentile2.058489609 × 1012
Maximum2.212868847 × 1012
Range2.024677618 × 1012
Interquartile range (IQR)1.095908757 × 1012

Descriptive statistics

Standard deviation6.454446507 × 1011
Coefficient of variation (CV)0.6722907445
Kurtosis-1.176948778
Mean9.600677326 × 1011
Median Absolute Deviation (MAD)4.057983848 × 1011
Skewness0.5165174748
Sum3.072216744 × 1013
Variance4.165987971 × 1023
MonotonicityNot monotonic
2022-07-04T13:42:05.965796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
5.065885684 × 10111
 
3.1%
5.157807257 × 10111
 
3.1%
1.639593354 × 10121
 
3.1%
1.616937756 × 10121
 
3.1%
1.532146087 × 10121
 
3.1%
1.241290379 × 10121
 
3.1%
1.325732263 × 10121
 
3.1%
1.991279765 × 10121
 
3.1%
2.212868847 × 10121
 
3.1%
2.140634974 × 10121
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
1.881912284 × 10111
3.1%
2.529736422 × 10111
3.1%
2.591657869 × 10111
3.1%
3.023639706 × 10111
3.1%
3.388874944 × 10111
3.1%
3.862903907 × 10111
3.1%
3.921655857 × 10111
3.1%
3.932373012 × 10111
3.1%
3.962373542 × 10111
3.1%
4.171770707 × 10111
3.1%
ValueCountFrequency (%)
2.212868847 × 10121
3.1%
2.140634974 × 10121
3.1%
1.991279765 × 10121
3.1%
1.985526208 × 10121
3.1%
1.639593354 × 10121
3.1%
1.616937756 × 10121
3.1%
1.614363888 × 10121
3.1%
1.532146087 × 10121
3.1%
1.477812768 × 10121
3.1%
1.453317165 × 10121
3.1%

gni_usa
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.288687662 × 1013
Minimum5.598381 × 1012
Maximum2.170865 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:42:06.079822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5.598381 × 1012
5-th percentile6.009243 × 1012
Q18.45342775 × 1012
median1.27420795 × 1013
Q31.6803779 × 1013
95-th percentile2.109971455 × 1013
Maximum2.170865 × 1013
Range1.6110269 × 1013
Interquartile range (IQR)8.35035125 × 1012

Descriptive statistics

Standard deviation5.040956735 × 1012
Coefficient of variation (CV)0.3911697832
Kurtosis-1.179244013
Mean1.288687662 × 1013
Median Absolute Deviation (MAD)4.2995315 × 1012
Skewness0.1888959459
Sum4.12380052 × 1014
Variance2.541124481 × 1025
MonotonicityNot monotonic
2022-07-04T13:42:06.196849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
5.598381 × 10121
 
3.1%
5.90229 × 10121
 
3.1%
2.170865 × 10131
 
3.1%
2.0946778 × 10131
 
3.1%
1.9893073 × 10131
 
3.1%
1.9020479 × 10131
 
3.1%
1.866091 × 10131
 
3.1%
1.8043094 × 10131
 
3.1%
1.7188331 × 10131
 
3.1%
1.6675595 × 10131
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
5.598381 × 10121
3.1%
5.90229 × 10121
3.1%
6.09675 × 10121
3.1%
6.435469 × 10121
3.1%
6.733768 × 10121
3.1%
7.170251 × 10121
3.1%
7.574691 × 10121
3.1%
8.045907 × 10121
3.1%
8.589268 × 10121
3.1%
9.135464 × 10121
3.1%
ValueCountFrequency (%)
2.170865 × 10131
3.1%
2.1286637 × 10131
3.1%
2.0946778 × 10131
3.1%
1.9893073 × 10131
3.1%
1.9020479 × 10131
3.1%
1.866091 × 10131
3.1%
1.8043094 × 10131
3.1%
1.7188331 × 10131
3.1%
1.6675595 × 10131
3.1%
1.5849978 × 10131
3.1%

gni_gbr
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.124018465 × 1012
Minimum9.518875474 × 1011
Maximum3.09115046 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:42:06.312875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum9.518875474 × 1011
5-th percentile1.108222766 × 1012
Q11.49585018 × 1012
median2.426908917 × 1012
Q32.720238901 × 1012
95-th percentile2.961364592 × 1012
Maximum3.09115046 × 1012
Range2.139262913 × 1012
Interquartile range (IQR)1.224388721 × 1012

Descriptive statistics

Standard deviation7.049562613 × 1011
Coefficient of variation (CV)0.331897426
Kurtosis-1.568681049
Mean2.124018465 × 1012
Median Absolute Deviation (MAD)5.400803355 × 1011
Skewness-0.275242605
Sum6.796859088 × 1013
Variance4.969633304 × 1023
MonotonicityNot monotonic
2022-07-04T13:42:06.428901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9.518875474 × 10111
 
3.1%
1.116245414 × 10121
 
3.1%
2.862006504 × 10121
 
3.1%
2.860062056 × 10121
 
3.1%
2.664014258 × 10121
 
3.1%
2.654615868 × 10121
 
3.1%
2.886069892 × 10121
 
3.1%
3.023235858 × 10121
 
3.1%
2.746456576 × 10121
 
3.1%
2.69110987 × 10121
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
9.518875474 × 10111
3.1%
1.098417308 × 10121
3.1%
1.116245414 × 10121
3.1%
1.169938034 × 10121
3.1%
1.199798307 × 10121
3.1%
1.221728958 × 10121
3.1%
1.304551338 × 10121
3.1%
1.373976732 × 10121
3.1%
1.536474663 × 10121
3.1%
1.655531884 × 10121
3.1%
ValueCountFrequency (%)
3.09115046 × 10121
3.1%
3.023235858 × 10121
3.1%
2.910742647 × 10121
3.1%
2.886069892 × 10121
3.1%
2.862006504 × 10121
3.1%
2.860062056 × 10121
3.1%
2.746456576 × 10121
3.1%
2.723174555 × 10121
3.1%
2.71926035 × 10121
3.1%
2.69110987 × 10121
3.1%

gni_bra
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.193270155 × 1012
Minimum3.219970471 × 1011
Maximum2.546425812 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:42:06.540926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3.219970471 × 1011
5-th percentile3.483504856 × 1011
Q15.405320747 × 1011
median8.67355989 × 1011
Q31.777211765 × 1012
95-th percentile2.419540482 × 1012
Maximum2.546425812 × 1012
Range2.224428765 × 1012
Interquartile range (IQR)1.236679691 × 1012

Descriptive statistics

Standard deviation7.364792935 × 1011
Coefficient of variation (CV)0.6171940949
Kurtosis-1.228555528
Mean1.193270155 × 1012
Median Absolute Deviation (MAD)5.04835205 × 1011
Skewness0.4702997394
Sum3.818464497 × 1013
Variance5.424017498 × 1023
MonotonicityNot monotonic
2022-07-04T13:42:06.652952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
3.657118395 × 10111
 
3.1%
3.803390364 × 10111
 
3.1%
1.816016547 × 10121
 
3.1%
1.858109689 × 10121
 
3.1%
2.02034506 × 10121
 
3.1%
1.754150399 × 10121
 
3.1%
1.764276838 × 10121
 
3.1%
2.406617082 × 10121
 
3.1%
2.435335749 × 10121
 
3.1%
2.401351848 × 10121
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
3.219970471 × 10111
3.1%
3.349314111 × 10111
3.1%
3.593297284 × 10111
3.1%
3.657118395 × 10111
3.1%
3.803390364 × 10111
3.1%
4.92077235 × 10111
3.1%
5.166077625 × 10111
3.1%
5.400986896 × 10111
3.1%
5.406765364 × 10111
3.1%
5.811609708 × 10111
3.1%
ValueCountFrequency (%)
2.546425812 × 10121
3.1%
2.435335749 × 10121
3.1%
2.406617082 × 10121
3.1%
2.401351848 × 10121
3.1%
2.138593266 × 10121
3.1%
2.02034506 × 10121
3.1%
1.858109689 × 10121
3.1%
1.816016547 × 10121
3.1%
1.764276838 × 10121
3.1%
1.754150399 × 10121
3.1%

gni_ind
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.131755513 × 1012
Minimum2.659954009 × 1011
Maximum2.804313596 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:42:06.761976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.659954009 × 1011
5-th percentile2.800396626 × 1011
Q14.065385461 × 1011
median7.59325783 × 1011
Q31.813664422 × 1012
95-th percentile2.650764558 × 1012
Maximum2.804313596 × 1012
Range2.538318195 × 1012
Interquartile range (IQR)1.407125876 × 1012

Descriptive statistics

Standard deviation8.617779525 × 1011
Coefficient of variation (CV)0.7614524008
Kurtosis-1.026175239
Mean1.131755513 × 1012
Median Absolute Deviation (MAD)4.595188862 × 1011
Skewness0.6738955527
Sum3.621617642 × 1013
Variance7.426612395 × 1023
MonotonicityNot monotonic
2022-07-04T13:42:06.874002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
2.926028745 × 10111
 
3.1%
3.167753325 × 10111
 
3.1%
2.804313596 × 10121
 
3.1%
2.673994353 × 10121
 
3.1%
2.622799775 × 10121
 
3.1%
2.247940124 × 10121
 
3.1%
2.079182326 × 10121
 
3.1%
2.015015377 × 10121
 
3.1%
1.833601557 × 10121
 
3.1%
1.806177662 × 10121
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
2.659954009 × 10111
3.1%
2.754445103 × 10111
3.1%
2.837993328 × 10111
3.1%
2.926028745 × 10111
3.1%
3.167753325 × 10111
3.1%
3.231087178 × 10111
3.1%
3.562523312 × 10111
3.1%
3.89212067 × 10111
3.1%
4.123140391 × 10111
3.1%
4.17792938 × 10111
3.1%
ValueCountFrequency (%)
2.804313596 × 10121
3.1%
2.673994353 × 10121
3.1%
2.631758362 × 10121
3.1%
2.622799775 × 10121
3.1%
2.247940124 × 10121
3.1%
2.079182326 × 10121
3.1%
2.015015377 × 10121
3.1%
1.833601557 × 10121
3.1%
1.80701871 × 10121
3.1%
1.806177662 × 10121
3.1%

gni_mex
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.022802641 × 1011
Minimum2.130984487 × 1011
Maximum1.283058447 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:42:06.982026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.130984487 × 1011
5-th percentile2.811765576 × 1011
Q15.070346289 × 1011
median8.148756312 × 1011
Q31.102955008 × 1012
95-th percentile1.234747279 × 1012
Maximum1.283058447 × 1012
Range1.069959999 × 1012
Interquartile range (IQR)5.959203794 × 1011

Descriptive statistics

Standard deviation3.364702429 × 1011
Coefficient of variation (CV)0.4193923968
Kurtosis-1.332674286
Mean8.022802641 × 1011
Median Absolute Deviation (MAD)3.079073512 × 1011
Skewness-0.2490834394
Sum2.567296845 × 1013
Variance1.132122243 × 1023
MonotonicityNot monotonic
2022-07-04T13:42:07.095052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
2.130984487 × 10111
 
3.1%
2.526276748 × 10111
 
3.1%
1.232604423 × 10121
 
3.1%
1.189348943 × 10121
 
3.1%
1.128823076 × 10121
 
3.1%
1.049583161 × 10121
 
3.1%
1.141602673 × 10121
 
3.1%
1.283058447 × 10121
 
3.1%
1.237366326 × 10121
 
3.1%
1.1778852 × 10121
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
2.130984487 × 10111
3.1%
2.526276748 × 10111
3.1%
3.045347345 × 10111
3.1%
3.467154056 × 10111
3.1%
3.535627492 × 10111
3.1%
3.969917809 × 10111
3.1%
4.876112449 × 10111
3.1%
4.891133939 × 10111
3.1%
5.130083739 × 10111
3.1%
5.151570508 × 10111
3.1%
ValueCountFrequency (%)
1.283058447 × 10121
3.1%
1.237366326 × 10121
3.1%
1.232604423 × 10121
3.1%
1.189348943 × 10121
3.1%
1.1778852 × 10121
3.1%
1.161743714 × 10121
3.1%
1.141602673 × 10121
3.1%
1.128823076 × 10121
3.1%
1.094332319 × 10121
3.1%
1.05114165 × 10121
3.1%

gni_zaf
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.546077564 × 1011
Minimum1.046861461 × 1011
Maximum4.474816508 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:42:07.208077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.046861461 × 1011
5-th percentile1.242657788 × 1011
Q11.485119128 × 1011
median2.677110966 × 1011
Q33.467932096 × 1011
95-th percentile4.156610982 × 1011
Maximum4.474816508 × 1011
Range3.427955046 × 1011
Interquartile range (IQR)1.982812968 × 1011

Descriptive statistics

Standard deviation1.114274204 × 1011
Coefficient of variation (CV)0.4376434637
Kurtosis-1.590258679
Mean2.546077564 × 1011
Median Absolute Deviation (MAD)1.135799078 × 1011
Skewness0.1705327823
Sum8.147448204 × 1012
Variance1.241607002 × 1022
MonotonicityNot monotonic
2022-07-04T13:42:07.322103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1.046861461 × 10111
 
3.1%
1.217807996 × 10111
 
3.1%
3.782507337 × 10111
 
3.1%
3.935533054 × 10111
 
3.1%
3.708729822 × 10111
 
3.1%
3.152940787 × 10111
 
3.1%
3.387666187 × 10111
 
3.1%
3.717452347 × 10111
 
3.1%
3.911947119 × 10111
 
3.1%
4.235167341 × 10111
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
1.046861461 × 10111
3.1%
1.217807996 × 10111
3.1%
1.262989436 × 10111
3.1%
1.316922098 × 10111
3.1%
1.319961612 × 10111
3.1%
1.440112202 × 10111
3.1%
1.445342164 × 10111
3.1%
1.483082781 × 10111
3.1%
1.48579791 × 10111
3.1%
1.498191824 × 10111
3.1%
ValueCountFrequency (%)
4.474816508 × 10111
3.1%
4.235167341 × 10111
3.1%
4.092337597 × 10111
3.1%
3.935533054 × 10111
3.1%
3.911947119 × 10111
3.1%
3.782507337 × 10111
3.1%
3.717452347 × 10111
3.1%
3.708729822 × 10111
3.1%
3.387666187 × 10111
3.1%
3.298187076 × 10111
3.1%

gni_chn
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.755707478 × 1012
Minimum3.479425873 × 1011
Maximum1.458328455 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:42:07.438129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3.479425873 × 1011
5-th percentile3.741040383 × 1011
Q19.257776416 × 1011
median2.110036298 × 1012
Q38.757454055 × 1012
95-th percentile1.401655144 × 1013
Maximum1.458328455 × 1013
Range1.423534196 × 1013
Interquartile range (IQR)7.831676413 × 1012

Descriptive statistics

Standard deviation4.871204961 × 1012
Coefficient of variation (CV)1.024286078
Kurtosis-0.786162509
Mean4.755707478 × 1012
Median Absolute Deviation (MAD)1.704394925 × 1012
Skewness0.8598157218
Sum1.521826393 × 1014
Variance2.372863777 × 1025
MonotonicityStrictly increasing
2022-07-04T13:42:07.653178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
3.479425873 × 10111
 
3.1%
3.618227837 × 10111
 
3.1%
1.423992824 × 10131
 
3.1%
1.38337886 × 10131
 
3.1%
1.229427857 × 10131
 
3.1%
1.117757625 × 10131
 
3.1%
1.100877107 × 10131
 
3.1%
1.048898252 × 10131
 
3.1%
9.492579024 × 10121
 
3.1%
8.512412398 × 10121
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
3.479425873 × 10111
3.1%
3.618227837 × 10111
3.1%
3.841523376 × 10111
3.1%
4.271304099 × 10111
3.1%
4.438089372 × 10111
3.1%
5.6328867 × 10111
3.1%
7.227734752 × 10111
3.1%
8.513097175 × 10111
3.1%
9.50600283 × 10111
3.1%
1.012399392 × 10121
3.1%
ValueCountFrequency (%)
1.458328455 × 10131
3.1%
1.423992824 × 10131
3.1%
1.38337886 × 10131
3.1%
1.229427857 × 10131
3.1%
1.117757625 × 10131
3.1%
1.100877107 × 10131
3.1%
1.048898252 × 10131
3.1%
9.492579024 × 10121
3.1%
8.512412398 × 10121
3.1%
7.481123497 × 10121
3.1%

gni_wld
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.11501314 × 1013
Minimum2.018565816 × 1013
Maximum8.767011266 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2022-07-04T13:42:07.761202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.018565816 × 1013
5-th percentile2.31970368 × 1013
Q13.152466415 × 1013
median4.590217239 × 1013
Q37.538966843 × 1013
95-th percentile8.565818624 × 1013
Maximum8.767011266 × 1013
Range6.74844545 × 1013
Interquartile range (IQR)4.386500428 × 1013

Descriptive statistics

Standard deviation2.300360824 × 1013
Coefficient of variation (CV)0.4497272561
Kurtosis-1.573043174
Mean5.11501314 × 1013
Median Absolute Deviation (MAD)1.915476053 × 1013
Skewness0.2564267497
Sum1.636804205 × 1015
Variance5.291659921 × 1026
MonotonicityNot monotonic
2022-07-04T13:42:07.871227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
2.018565816 × 10131
 
3.1%
2.265298775 × 10131
 
3.1%
8.767011266 × 10131
 
3.1%
8.646916568 × 10131
 
3.1%
8.15637375 × 10131
 
3.1%
7.649008638 × 10131
 
3.1%
7.532880546 × 10131
 
3.1%
7.991305533 × 10131
 
3.1%
7.754919095 × 10131
 
3.1%
7.557225735 × 10131
 
3.1%
Other values (22)22
68.8%
ValueCountFrequency (%)
2.018565816 × 10131
3.1%
2.265298775 × 10131
3.1%
2.364216784 × 10131
3.1%
2.529376357 × 10131
3.1%
2.570705435 × 10131
3.1%
2.778776938 × 10131
3.1%
3.089317498 × 10131
3.1%
3.151395883 × 10131
3.1%
3.15282326 × 10131
3.1%
3.159403835 × 10131
3.1%
ValueCountFrequency (%)
8.767011266 × 10131
3.1%
8.646916568 × 10131
3.1%
8.499465761 × 10131
3.1%
8.15637375 × 10131
3.1%
7.991305533 × 10131
3.1%
7.754919095 × 10131
3.1%
7.649008638 × 10131
3.1%
7.557225735 × 10131
3.1%
7.532880546 × 10131
3.1%
7.371030799 × 10131
3.1%

Interactions

2022-07-04T13:42:01.498623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:34.220223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:35.760573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:37.417946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:39.106328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:40.794710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:42.413075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:44.153469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:45.889862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:47.671264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:49.314635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:51.058030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:52.769417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:54.528047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:56.151414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:57.910811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:59.628200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:42:01.606647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:34.311244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:35.852593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:37.505966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:39.194348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:40.888731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:42.506096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:44.246490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:45.987884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:47.766286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:49.407656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:51.148050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:52.863438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:54.620068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:56.247436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:58.000832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:59.723221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:42:01.708670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:34.397263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:35.943613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:37.593986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:39.282367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:40.980752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:42.599117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:44.338511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:46.084906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:47.860307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:49.498677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:51.235070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:52.959460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:54.709088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:56.341457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:58.094853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:59.813241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:42:01.824696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:34.484283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:36.032634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:37.682006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:39.373388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:41.075773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:42.693139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:44.431531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:46.183928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:47.954328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:49.592698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:51.328091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:53.051481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:54.801109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:56.435478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:58.186874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:59.903262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:42:01.922719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:34.572303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:36.121653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:37.771026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:39.464409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:41.170795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:42.786160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:44.522552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:46.281950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:48.046349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:49.797744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:51.421112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:53.144501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:54.891129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:56.529499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:58.281895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:59.998283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:42:02.027742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:34.663324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:36.325699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:37.870049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:39.558430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:41.268817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:42.987205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:44.622575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:46.384973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:48.145372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:49.896767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:51.517133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:53.241524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:54.994153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:56.629522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:58.379918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:42:00.095305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:42:02.124764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:34.756345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:36.419721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:37.969071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:39.651451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:41.369840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:43.081226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:44.723598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:46.484996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:48.246394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:49.995790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:51.609154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:53.331776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:55.089174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:56.838569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:58.475939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:42:00.205330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:42:02.224787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:34.851367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:36.510741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:38.069094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:39.743472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:41.467861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:43.177248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:44.822620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:46.583018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:48.345417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:50.091811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:51.703175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:53.423797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:55.182195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:56.932590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:58.572961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:42:00.306353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:42:02.321808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:34.947388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:36.600761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:38.164115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:39.835493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:41.563884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:43.280271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:44.917642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:46.682041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:48.442438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:50.187833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:51.797197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:53.534822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:55.281217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:57.025612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:58.667983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:42:00.408377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:42:02.416830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:35.039408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:36.692783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:38.260137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:39.929514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:41.660905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:43.378294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:45.016664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:46.786064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:48.540461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:50.290856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:52.011245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:53.632844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:55.383240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:57.130635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:58.763004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:42:00.508399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:42:02.512852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:35.131429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:36.784803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:38.354158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:40.023535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:41.758927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:43.477316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:45.231712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:46.885086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:48.638483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:50.390879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:52.108267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:53.731867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:55.480262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:57.235660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:58.860026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:42:00.615423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:42:02.605873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:35.217449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:36.871823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:38.558205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:40.114557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:41.849948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:43.576338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:45.323733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:46.978108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:48.730503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:50.483900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:52.205289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:53.827889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:55.580286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:57.329680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:58.950047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:42:00.722448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:42:02.695893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:35.305469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:36.960843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:38.649225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:40.209578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:41.940969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:43.667359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:45.414754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:47.076130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:48.824525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:50.576922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:52.299311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:53.922910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:55.673306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:57.423701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:59.157093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:42:00.823470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:42:02.786914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:35.392488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:37.046862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:38.738245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:40.307600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:42.032989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:43.757379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:45.507775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:47.178154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:48.917546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:50.670942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:52.394332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:54.016933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:55.763326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:57.516723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:59.248114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:42:00.939497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:42:02.878935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:35.480508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:37.136883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:38.828265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:40.400621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:42.126011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:43.852402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:45.600796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:47.275175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:49.012567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:50.764964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:52.488353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:54.112953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:55.861348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:57.617745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:59.342135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:42:01.047520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:42:02.973956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:35.573529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:37.227903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:38.920286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:40.494641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:42.222032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:43.950423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:45.695817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:47.374197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:49.111590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:50.861986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:52.586375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:54.212976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:55.964371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:57.715767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:59.441157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:42:01.146543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:42:03.067977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:35.669552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:37.323925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:39.015307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:40.586662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:42.317054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:44.053447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:45.789839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:47.576242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:49.215613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:50.962008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:52.677395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:54.433025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:56.058394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:57.814790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:59.535178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:42:01.244565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-07-04T13:42:07.981252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-04T13:42:08.121284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-04T13:42:08.261770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-04T13:42:08.404802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-04T13:42:03.209009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-04T13:42:03.371046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

yearcivil_conflictsstate_interventionconflict_between_statestotalgni_gergni_fragni_itagni_jpngni_cangni_rusgni_usagni_gbrgni_bragni_indgni_mexgni_zafgni_chngni_wld
01970-01-01 00:00:00.0000019893352401.414250e+121.029131e+129.240834e+113.078857e+125.477095e+115.065886e+115.598381e+129.518875e+113.657118e+112.926029e+112.130984e+111.046861e+113.479426e+112.018566e+13
11970-01-01 00:00:00.0000019904432491.790872e+121.273598e+121.166755e+123.156842e+125.746272e+115.157807e+115.902290e+121.116245e+123.803390e+113.167753e+112.526277e+111.217808e+113.618228e+112.265299e+13
21970-01-01 00:00:00.0000019914822521.875828e+121.272602e+121.228446e+123.609952e+125.922283e+115.165452e+116.096750e+121.169938e+123.349314e+112.659954e+113.045347e+111.319962e+113.841523e+112.364217e+13
31970-01-01 00:00:00.0000019924441492.137944e+121.406932e+121.298792e+123.942654e+125.730802e+114.557897e+116.435469e+121.221729e+123.219970e+112.837993e+113.535627e+111.440112e+114.271304e+112.529376e+13
41970-01-01 00:00:00.0000019933760432.074906e+121.329199e+121.049906e+124.493789e+125.593721e+114.306245e+116.733768e+121.098417e+123.593297e+112.754445e+114.891134e+111.445342e+114.438089e+112.570705e+13
51970-01-01 00:00:00.0000019944620482.204519e+121.398516e+121.082154e+125.040360e+125.591725e+113.932373e+117.170251e+121.199798e+125.166078e+113.231087e+115.151571e+111.510909e+115.632887e+112.778777e+13
61970-01-01 00:00:00.0000019953721402.582252e+121.606456e+121.160407e+125.592683e+125.850867e+113.921656e+117.574691e+121.304551e+127.585862e+113.562523e+113.467154e+111.688605e+117.227735e+113.089317e+13
71970-01-01 00:00:00.0000019963632412.497603e+121.619352e+121.299726e+124.982754e+126.096560e+113.862904e+118.045907e+121.373977e+128.390521e+113.892121e+113.969918e+111.601247e+118.513097e+113.159404e+13
81970-01-01 00:00:00.0000019973541402.207766e+121.470553e+121.236195e+124.551411e+126.346909e+113.962374e+118.589268e+121.536475e+128.686308e+114.123140e+114.876112e+111.657553e+119.506003e+113.152823e+13
91970-01-01 00:00:00.0000019983262402.226181e+121.522697e+121.263071e+124.148976e+126.126748e+112.591658e+119.135464e+121.660688e+128.458709e+114.177929e+115.130084e+111.498192e+111.012399e+123.151396e+13

Last rows

yearcivil_conflictsstate_interventionconflict_between_statestotalgni_gergni_fragni_itagni_jpngni_cangni_rusgni_usagni_gbrgni_bragni_indgni_mexgni_zafgni_chngni_wld
221970-01-01 00:00:00.0000020113071383.845325e+122.941328e+122.289190e+126.414397e+121.759454e+121.985526e+121.584998e+132.684173e+122.546426e+121.807019e+121.161744e+124.474817e+117.481123e+127.371031e+13
231970-01-01 00:00:00.0000020122391333.611772e+122.742349e+122.084407e+126.445537e+121.795920e+122.140635e+121.667560e+132.691110e+122.401352e+121.806178e+121.177885e+124.235167e+118.512412e+127.557226e+13
241970-01-01 00:00:00.0000020132790363.820264e+122.874860e+122.138212e+125.391606e+121.818471e+122.212869e+121.718833e+132.746457e+122.435336e+121.833602e+121.237366e+123.911947e+119.492579e+127.754919e+13
251970-01-01 00:00:00.00000201428131423.967012e+122.917865e+122.162198e+125.079021e+121.776572e+121.991280e+121.804309e+133.023236e+122.406617e+122.015015e+121.283058e+123.717452e+111.048898e+137.991306e+13
261970-01-01 00:00:00.00000201531201523.434101e+122.491865e+121.823941e+124.619771e+121.532664e+121.325732e+121.866091e+132.886070e+121.764277e+122.079182e+121.141603e+123.387666e+111.100877e+137.532881e+13
271970-01-01 00:00:00.00000201634182543.555931e+122.525277e+121.882461e+125.177795e+121.509328e+121.241290e+121.902048e+132.654616e+121.754150e+122.247940e+121.049583e+123.152941e+111.117758e+137.649009e+13
281970-01-01 00:00:00.00000201729211513.778789e+122.653805e+121.972735e+125.113252e+121.628292e+121.532146e+121.989307e+132.664014e+122.020345e+122.622800e+121.128823e+123.708730e+111.229428e+138.156374e+13
291970-01-01 00:00:00.00000201831192524.105202e+122.855661e+122.115037e+125.230632e+121.695997e+121.616938e+122.094678e+132.860062e+121.858110e+122.673994e+121.189349e+123.935533e+111.383379e+138.646917e+13
301970-01-01 00:00:00.00000201928252554.014394e+122.787417e+122.028318e+125.323445e+121.719639e+121.639593e+122.170865e+132.862007e+121.816017e+122.804314e+121.232604e+123.782507e+111.423993e+138.767011e+13
311970-01-01 00:00:00.00000202028253563.953466e+122.671814e+121.915963e+125.222887e+121.627049e+121.453317e+122.128664e+132.723175e+121.410302e+122.631758e+121.051142e+123.298187e+111.458328e+138.499466e+13